Transcript No Slide Title

An assessment of correlation between
vegetation parameters measured on the
ground and endmember fractions from
remotely sensed data of varying spatial
resolution
Seth Peterson
Department of Geography,
University of California, Santa Barbara
Acknowledgements:
USFS - 4 years of funding
Presentation Overview
1) Fire / fuel loads
2) SMA
3) Sample Endmember fractions
4) MESMA
5) Sample Endmember fraction / biomass correlations
Why is fire important?
- Fuel loads have increased
- Urban encroachment into wildlands
- These processes may be different for different ecosystems
(study sites are in 5 western states)
How can we study fire fuel loads?
- Massive amounts of ground-based sampling
- Small, well-designed ground-based studies to calibrate
large area remotely sensed scenes
- Correlate different indices and products from image
processing techniques with ground-based data
Spectral Mixture Analysis (SMA)
-Expresses pixel values as mixtures of the scene components,
called endmembers (EMs)
-Typical EMs used are:
-green vegetation (GV -- e.g. green leaves)
-nonphotosynthetic vegetation (NPV -- e.g. bark, branches, litter)
-rocks, soils
-shade
GV
NPV
Landsat TM imagery
for MCAS Miramar
with Endmember
fraction images
Soil
Shade
The mixed pixel problem / Endmember analysis
GV
Soil
NPV
ADAR data, 1 m pixels
Landsat TM data, 30 m pixels
Feature space plots for the MCAS Miramar
Landsat TM scene, with approximate EM locations
GV
soil
Band 4
Band 7
soil
NPV
shade
shade
GV
Band 3
Band 4
Multiple Endmember SMA (MESMA)
- Allows for flexibility in the number of EMs used to model each pixel
- Allows for flexibility in the type of EMs used to model each pixel
Band 7
-Modeled EM fractions will be most accurate when the fewest,
most appropriate EMs are used to model each pixel
soil_2
NPV_2
NPV_1
soil_1
shade_photo
shade_phyto
Band 4
GV_2
GV_1
EM Fractions vs. time for stands of chamise chaparral
100
100
GV
90
80
80
Endmem ber Fraction
Endmem ber Fraction
NPV
90
70
60
50
40
30
20
70
60
50
40
30
20
10
10
0
0
0
20
40
60
80
100
120
0
20
Stand Age, years
100
80
100
100
120
Shade
90
80
80
Endmem ber Fraction
Endmem ber Fraction
60
Stand Age, years
Soil
90
40
70
60
50
40
30
20
70
60
50
40
30
20
10
10
0
0
0
20
40
60
80
Stand Age, years
100
120
0
20
40
60
80
Stand Age, years
100
120
Summary
Fire is a problem
Remote Sensing is one way to look at it